# T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease Progression

arXiv: https://arxiv.org/abs/2302.12619 · HTML: https://mlsanity.com/paper/2302.12619/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Towards K-means-friendly Spaces: Simultaneous Deep Learning and Clustering](https://mlsanity.com/paper/1610.04794/) (2016)
- [Temporal Phenotyping using Deep Predictive Clustering of Disease Progression](https://mlsanity.com/paper/2006.08600/) (2020)
- [Clustering Interval-Censored Time-Series for Disease Phenotyping](https://mlsanity.com/paper/2102.07005/) (2021)
- [Mixture of Input-Output Hidden Markov Models for Heterogeneous Disease Progression Modeling](https://mlsanity.com/paper/2207.11846/) (2022)

## Built on by (most cited first, 9 of 9)

- [MIRA: Medical Time Series Foundation Model for Real-World Health Data](https://mlsanity.com/paper/2506.07584/) (2025)
- [Temporal Supervised Contrastive Learning for Modeling Patient Risk Progression](https://mlsanity.com/paper/2312.05933/) (2023)
- [On the Importance of Step-wise Embeddings for Heterogeneous Clinical Time-Series](https://mlsanity.com/paper/2311.08902/) (2023)
- [Fast Mining and Dynamic Time-to-Event Prediction over Multi-sensor Data Streams](https://mlsanity.com/paper/2601.04741/) (2026)
- [CSRA: Controlled Spectral Residual Augmentation for Robust Sepsis Prediction](https://mlsanity.com/paper/2604.14532/) (2026)
- [SOTER: A Generative Time-Series Foundation Model for Wearable Human Physiological Signals](https://mlsanity.com/paper/2609.16804/) (2026)
- [Clustering of Disease Trajectories with Explainable Machine Learning: A Case Study on Postoperative Delirium Phenotypes](https://mlsanity.com/paper/2405.03327/) (2024)
- [Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis](https://mlsanity.com/paper/2407.11427/) (2024)
- [ChronoMedKG: A Temporally-Grounded Biomedical Knowledge Graph and Benchmark for Clinical Reasoning](https://mlsanity.com/paper/2605.22734/) (2026)

## Abstract (arXiv)

Clustering time-series data in healthcare is crucial for clinical phenotyping to understand patients' disease progression patterns and to design treatment guidelines tailored to homogeneous patient subgroups. While rich temporal dynamics enable the discovery of potential clusters beyond static correlations, two major challenges remain outstanding: i) discovery of predictive patterns from many potential temporal correlations in the multi-variate time-series data and ii) association of individual temporal patterns to the target label distribution that best characterizes the underlying clinical progression. To address such challenges, we develop a novel temporal clustering method, T-Phenotype, to discover phenotypes of predictive temporal patterns from labeled time-series data. We introduce an efficient representation learning approach in frequency domain that can encode variable-length, irregularly-sampled time-series into a unified representation space, which is then applied to identify various temporal patterns that potentially contribute to the target label using a new notion of path-based similarity. Throughout the experiments on synthetic and real-world datasets, we show that T-Phenotype achieves the best phenotype discovery performance over all the evaluated baselines. We further demonstrate the utility of T-Phenotype by uncovering clinically meaningful patient subgroups characterized by unique temporal patterns.